In wireless communication, different transmission modes have been defined for downlink transmission in downlink channels. In principle, different transmission modes are suitable for different scenarios (antenna setup, radio environment, terminal speed, etc).
A conventional method for transmission mode switching is disclosed in A. Forenza, A. Pandharipande, etc, “Adaptive MIMO Transmission Scheme: Exploiting the Spatial Selectivity of Wireless Channels”, IEEE VTC, pp. 2188-2192, vol. 5, Stockholm, Sweden, 2005, wherein an adaptive transmission scheme was proposed for multiple-input and multiple-output (MIMO) systems. The condition number of the spatial correlation matrix was used as an indicator of the spatial selectivity of the channel. Meanwhile, distribution of the condition number is used to identify the prevailing channel environment. Depending on the identified channel state, it adaptively chooses the MIMO transmission scheme, among spatial multiplexing, double space-time transmit diversity, and beamforming to maximize the throughput.
The major drawback with this solution is complexity. First, the conditional number of the spatial correlation matrix has to be calculated for each user which relies on eigen value decomposition (EVD). Furthermore, in order to get an accurate distribution of the condition number, a large number of EVD computations are required. This imposes a high burden on the system especially at high system load. Second, for each channel state, different Signal to Interference and Noise Ratio (SINR) thresholds should be determined for each of the available modulation and coding scheme. This needs a lot of link-level simulations. Another problem with this solution is accuracy. It categorizes the propagation scenarios into four typical cases depending on the degree of spatial selectivity, where in reality the propagation environments are quite complicated and might not be fitted into these given categories.